The Applications of Radiomics in Precision Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 325

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Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
Interests: Artificial intelligence; cancer; deep learning; image segmentation and classification; machine learning; medical image analysis; personalized medicine; prognostication; radiomics; risk stratification
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Special Issue Information

Dear Colleagues,

Radiomics has emerged as a computational method for the high-dimensional high-throughput extraction and analysis of quantitative imaging biomarkers from medical images. Since its introduction in 2012, computer scientists, researchers, radiologists, and physicians have gravitated towards this new tool and exploited advanced methodologies to discover hidden features behind medical images. Radiomic features have been extensively investigated in a variety of diseases, including but also going beyond the field of oncology; its capabilities in divulging tissue heterogeneity on the basis of the distribution of imaging pixel/voxel intensity have been demonstrated to facilitate the clinical decision-making process and inform precision medical decisions. In particular, radiomics has great potential toward precision diagnosis, such as risk prediction for a particular disease, the classification of lesions, and the severity grading of a disease/symptom. This Special Issue aims to collect systematic reviews, basic research articles, and clinical/technical studies that employ radiomics for applications in the precision diagnosis of human diseases of all kinds.

Dr. Sai Kit Lam
Guest Editor

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Keywords

  • artificial intelligence in precision diagnosis
  • disease classification
  • early diagnosis
  • imaging biomarkers
  • radiomics

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Published Papers (1 paper)

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Research

13 pages, 1584 KiB  
Article
Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach
by Erdal Tasci, Ying Zhuge, Longze Zhang, Holly Ning, Jason Y. Cheng, Robert W. Miller, Kevin Camphausen and Andra V. Krauze
Diagnostics 2025, 15(10), 1292; https://doi.org/10.3390/diagnostics15101292 - 21 May 2025
Abstract
Background/Objectives: Glioblastoma (GBM) is a highly aggressive primary central nervous system tumor with a median survival of 14 months. MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status is a key biomarker as a prognostic indicator and a predictor of chemotherapy response in GBM. Patients [...] Read more.
Background/Objectives: Glioblastoma (GBM) is a highly aggressive primary central nervous system tumor with a median survival of 14 months. MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status is a key biomarker as a prognostic indicator and a predictor of chemotherapy response in GBM. Patients with MGMT methylated disease progress later and survive longer (median survival rate 22 vs. 15 months, respectively) as compared to patients with MGMT unmethylated disease. Patients with GBM undergo an MRI of the brain prior to diagnosis and following surgical resection for radiation therapy planning and ongoing follow-up. There is currently no imaging biomarker for GBM. Studies have attempted to connect MGMT methylation status to MRI imaging appearance to determine if brain MRI can be leveraged to provide MGMT status information non-invasively and more expeditiously. Methods: Artificial intelligence (AI) can identify MRI features that are not distinguishable to the human eye and can be linked to MGMT status. We employed the UPenn-GBM dataset patients for whom methylation status was available (n = 146), employing a novel radiomic method grounded in hybrid feature selection and weighting to predict MGMT methylation status. Results: The best MGMT classification and feature selection result obtained resulted in a mean accuracy rate value of 81.6% utilizing 101 selected features and five-fold cross-validation. Conclusions: This compared favorably with similar studies in the literature. Validation with external datasets remains critical to enhance generalizability and propagate robust results while reducing bias. Future directions include multi-channel data integration with radiomic features and deep and ensemble learning methods to improve predictive performance. Full article
(This article belongs to the Special Issue The Applications of Radiomics in Precision Diagnosis)
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